By gathering preferences of users and performing data analyzing and data mining, the accuracy of product information pushing can be effectively improved. In a conventional manner, the user preference degree over certain product is typically established by the user behavior. For example, the user behavior includes: clicking, collecting, purchasing. When a prediction is made to a unknown user preference degree value, a consideration on the factor of time is therefore missing. Suppose that a user purchased certain product one year ago and ceased to purchase the same this year. If a prediction of the user preference degree over the product this year is made according to the preference degree of such product of such user from last year, the prediction result apparently cannot reflect the real case. As such, it is a technical problem to be addressed to, in combination with the factor of time, perform an effective prediction over the user preference degree value of a specified product.